Failure to respond

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how can I determine failure to respond? For this assignment, I must review the “Failure to Respond” journal article and ascertain an understanding of the current trends in Nursing Education and Practice regarding entry-level preparation and transition into practice.


Abstract—Clinical judgment and decision-making is a

required component of professional nursing. Expert nurses are
known for their efficient and intuitive decision-making processes,
while novice nurses are known for more effortful and deliberate
decision-making processes. Despite taking longer to make
decisions, novices still have trouble with effective
decision-making. The aim of this paper is to review the factors
that contribute to clinical judgment and decision-making of
novice nurses. This was achieved by reviewing over two hundred
articles produced by searches through PsycINFO. These articles
used various methods of data collection, ranging from
observation to well-controlled experimentation, although the
majority of the studies were exploratory in nature. Factors that
influenced decision-making were categorized as either individual
or environmental factors. Individual factors captured elements
unique to the decision-maker and included factors such as
experience, cue recognition, and hypothesis updating. By
contrast, environmental factors captured elements surrounding
the decision-task. Among these factors were task complexity, time
pressure, and interruptions. The reliability and robustness of
these factors are discussed.

Keywords: novice nurses, clinical decision-making, clinical

reasoning, clinical judgment

ound clinical reasoning and clinical decision-making is
largely considered a “hallmark” of expert nursing
(Simmons, Lanuza, Fonteyn, Hicks, & Holm, 2003). The

ability to carry out competent decision-making is a critical and
fundamental aspect of professional nursing. Decision-making
abilities distinguish professional nurses from ancillary health
care workers (Hughes & Young, 1990). In professional health
care, it is often the case that decision consequences approach
high risks, leaving little room for errors. Furthermore, the
current health care environment has trended towards placing
more accountability and responsibilities on nurses (Simmons

Paper completed in August, 2012. This work was supported in part by the
National Council of State Boards of Nursing (NCSBN). Nursing clinical
decision-making: A literature review.

W. J. Muntean is with the Department of Psychology, University of

Correspondence concerning this paper should be addressed to William J.
Muntean, Department of Psychology, University of Oklahoma, Dale Hall
Tower, 455 West Lindsey Street, Norman, OK 73019. E-mail:
[email protected]

et al., 2003; Saintsing, Gibson, & Pennington, 2011; Ebright,
Urden, Patterson, & Chalko, 2004; Casey, Fink, Krugman, &
Propst, 2004; Hickey, 2009).

Nurses are at the forefront of patient care, usually the first
link in the causal chain between identifying complications and
eventual rescue (Thompson et al., 2008). This, coupled with
the increasing responsibilities, underscores the importance of
sound clinical reasoning and decision-making. Choosing
appropriate interventions accurately and timely is crucial
(Clarke & Aiken, 2003).

Brennan and colleagues estimate that up to 65% of adverse
events that hospital inpatients endure may be preventable—a
result of poor clinical decision-making (Brennan et al., 2004;
Leape, 2000). Hodgetts et al. (2002) report that 60% of cardiac
arrests suffered by inpatients during hospitalization could have
been prevented, with nearly half of those cases showing
clinical signs of deterioration recorded in the preceding 24
hours, but not acted on (as cited in Thompson et al., 2008).
Shockingly, the values recorded—but not acted upon—are the
part of the basic knowledge of nursing practice, and are
essential cues used to make clinical decisions (e.g., hear rate,
respiratory rate, and oxygenation; see Goldhill, 2001). Surely,
nurses must be aware that the decisions they make have
significant impact on the healthcare outcome of their patients,
yet these reports raise major concern (Long, Young, &
Shields, 2007; Dowding & Thompson, 2003). What factors
contributed to such lapses in clinical judgments?

Given the nature of the profession, nurses must perform at
high levels—but can this be expected of novice nurses who
just enter the field? A descriptive survey of employers of new
nurses found that, in general, newly licensed nurses tend to be
inadequately prepared to enter practice (Smith & Crawford
2002), with half the novice nurses being involved in errors of
nursing care (Saintsing et al., 2011; Smith & Crawford 2003;
Kenward & Zhong 2006). In addition, Saintsing et al. (2011)
reported that only 20% of employers were satisfied with
decision-making abilities of new nurses. Given the high
involvement in errors and the assumption that
decision-making is an integral part of nursing, it would
prudent to carefully inspect the factors contributing to clinical
decision-making in novice nurses. In what follows, I present a
review of the emerging themes that have been explored in
nursing clinical decision-making and highlight the known and
suspected influencers on clinical decision-making.

Nursing Clinical Decision-Making: A Literature

William J. Muntean


W. J. Muntean | Clinical decision-making 2


An evaluation of the peer-reviewed literature generated from
PsycINFO with various combinations of the terms
“decision-making”, “judgment”, “clinical”, “novice”, and
“nursing” was carried out. The following limits were placed
on the search: (1) articles must come from peer-reviewed
journals; (2) only English language publications were
reviewed; and (3) full text of the article must be available.
Using these criteria, the search produced an overwhelming set
of articles—over 1500 studies. Of these articles, roughly 800
were loosely related to nursing clinical decision-making and
were reviewed. This subset of articles produced about 200
articles that had strong relevance to clinical decision-making
and were subjected to a more detailed and thorough review.

The following paper summarizes research from the final
subset of articles. In addition to a database search, citations to
and from articles were also used. This led to the review of
several book chapters, but to foreshadow a general theme
found in the literature, most chapters are not reported because
of the highly subjective nature of the content. Overall, this
process uncovered three research themes on clinical
decision-making—research on factors that influence nurse
participation in clinical decision-making, research comparing
decision-making processes between novice and expert nurses,
and research on factors known (or suspected) to influence
decision-making in nursing.

The single primary objective of this literature review was to
uncover factors that influence clinical decision-making (either
positively or negatively) in first-year novice nurses. However,
there is a dearth of studies conducted with such a specific
research goal; studies either deviate on participants used or
focus on other aspects of clinical decision-making. There are
several likely reasons that research on clinical
decision-making of novice nurses is limited. First, there is a
lack of consistency as to what constitutes a novice nurse
(Simmons et al., 2003). Several researchers qualify nursing
students (perhaps inappropriately) as novices (Tanner,
Padrick, Westfall, & Putzier, 1987; Thiele, Holloway,
Murphy, & Pendarvis, 1991; Baxter & Rideout, 2006;
Lofmark, Smide, & Wikblad, 2006; Shin, 1998), whereas
others define it within a single year (Ebright et al., 2004;
Wainwright, Shepard, Harman, & Stephens, 2011; Saintsing et
al., 2011; Greenwood & King, 1995; Forneris &
Peden-McAlpine, 2007) and still others define it as within two
years (Hoffman, Aitken, & Duffield, 2009; Grobe, Drew, &
Fonteyn, 1991).

Second, a substantial number of clinical decision-making
researchers have seemingly focused on the development of
decision-making abilities and therefore include novice nurses
as a mere baseline comparison group (Chunta & Katrancha,
2010; Benner, Tanner, & Chesla, 1992). Lastly, researchers
focusing on the core decision-making process are more
interested in nurses whose decision-making abilities are
purportedly fully developed (e.g., expert nurses), which makes
the implicit assumption that all novice decision-making is
inferior and unstable (Buckingham & Adams, 2000a, 2000b).
Often times these studies are carried out on specialized nurses,
requiring more expertise and experience than most novice

nurses have (Kaasalainen et al., 2007; Marshall, West, &
Aitken, 2011; Monterosso et al., 2005). Despite the dispersive
focus of the field, studies that had strong implications for
novice decision-making were included and described

The three lines of research that emerged from the review are
intimately related and need to be considered collectively. For
instance, factors that influence the frequency of participation
in decision-making may have differential effects on expert and
novice nurses (Hoffman, Donoghue, & Duffield, 2004;
Prescott, Dennis, & Jacox, 1987). Frequency of
decision-making participation is assumed to play a critical
developmental role in clinical competency (see, e.g., Thiele et
al., 1991). Those who receive more opportunity in clinical
decision-making are provided with more feedback on their
decisions and interventions, ultimately leading to better
quality decisions in the future (Thiele, Baldwin, Hyde, Sloan,
& Strandquist, 1986). This is not to say that experience alone
accounts for the development of decision-making skills
(Benner, 1984), but instead it allows for more occurrences of
factors that contribute to clinical decision-making skill
development (Zinsmeister & Schafer, 2009).

Studies comparing novice and expert nurses are important
for understanding clinical decision-making. This line of
research focuses on the underlying decision-making process
involved when nurses make clinical decisions. Various
theoretical frameworks are put forth in the literature and each
is useful for investigating decision-making factors because the
frameworks break down the decision process into
subcomponents—providing simpler methods of investigating
influencers. For instance, expert nurses have been shown to
use more forward reasoning in decision-making (e.g., data
evaluation triggers a hypothesis), while novice nurses are
shown to use more backward reasoning (e.g., hypothesis
constrains data evaluation). Therefore, any manipulation
affecting the collection of information (e.g., the quality of
information, the ratio of confirming/disconfirming evidence to
a particular action plan, etc.) will differentially impact expert
and novice nurses in their ability to update their hypothesis.
Hence, offering a method to differentiate between novice and
expert nurses (Lamond, Crow, & Chase, 1996; Lauri &
Salantera, 1995).

The final theme in the literature review is research that
investigated factors contributing to clinical decision-making.
These studies tend to be qualitative in nature (e.g., focus
groups, think-aloud, observations) and use self-report
questionnaires or survey methods for data collection (Funk,
Tornquist, & Champagne, 1995), which might be problematic
because conclusions are drawn on an ad hoc, exploratory basis
(for a lengthier explanation, see Thompson, 1999a). That is,
researchers explore transcriptions of interview or observation
data and find general decision-making factors that are reported
by participants. No further confirmatory research is conducted
to determine whether the factors in question are discovered
through chance or are actually found in the nursing

Few studies employ experimental techniques (e.g.,
manipulation of variables, proper controls, randomization,
etc.). This speaks to the difficulty and complexity of
conducting nursing research in applied environments

W. J. Muntean | Clinical decision-making 3

(Dowding & Thompson, 2003; Aitken, Marshall, Elliott, &
Mckinley 2011). Although experimentation has the benefit of
controlling for nuisance variables (e.g., confounds) and
showing causality, it runs the risk of oversimplification. And
while reducing nursing environments to vignettes for the sake
of experimentation might show the basic processes of
decision-making, doing so can lose sight of the overall picture
of applicability. It is the classic argument of in vitro versus in
vivo—applied versus laboratory research. Therefore,
regardless of the exploratory nature of nursing clinical
decision-making research, these studies lay the groundwork
for future experiments to confirm the critical factors that
impact clinical judgment and decision-making.

Collectively, these three themes highlight two categories of
variables that impact nursing clinical decision-making,
individual factors (e.g., cue recognition, knowledge structure,
ability to update working hypothesis, communication, current
state of emotion, etc.) and environmental factors (e.g., task
complexity, time pressure, interruptions, professional
autonomy, etc.). Individual factors focus on the
decision-maker and various properties of information
processing. By contrast, environmental factors relate to the
to-be-processed information. For example, a nurse’s cue
recognition ability will directly impact the efficiency and
accuracy of their decisions—an individual factor. However,
task complexity— an environmental factor—affects the
presentation of cues and has an indirect impact on the
decision-maker. The agreement on these factors in the
literature is mixed. Some factors, such as task complexity,
have repeatedly been shown to impact clinical
decision-making (Corcoran, 1986a; Hicks, Merritt, & Elstein,
2003; Hughes & Young, 1990; Lewis, 1997). However, there
has been less agreement on other factors, such as education
level or experience (Sanford, Genrich, & Nowotny, 1992; del
Bueno, 1983; Shin, 1998; Bechtel, Smith, Printz, Gronseth,
1993). Where appropriate, reasons for disparate results are


As mentioned above, the majority of studies reviewed

implement qualitative methods, varying primarily between
either observational designs or think aloud protocols, although
there are a substantial amount of studies that collect data
through surveys. There are several issues with these methods
that are worth mentioning. First, for qualitative research,
regardless of the means of collection, data must be coded
either descriptively or thematically. This requires multiple
trained coders to ensure reliability in coding. Furthermore,
statistics should be provided as to the amount of agreement
between coders, also known as inter-rater reliability. Given
that the majority of nursing research is qualitative (Cullum,
1997; Thompson, McCaughan, Cullum, Sheldon, & Raynor,
2004; Thompson, 1999a), reliable coding is imperative so
results and conclusions are not contingent on researcher bias

or ambiguous constructs. However, nearly all articles
reviewed either failed to include multiple raters or included
multiple raters but provided no measure of inter-rater
reliability. This issue is so prevalent in the nursing clinical
decision-making literature that Thompson and colleagues
published a paper calling on researchers to be more
transparent in coding procedures (Thompson et al., 2004).

Employing questionnaires as a means of collecting data
affords the luxury of obtaining a large sample, but information
collected through this method is contingent on the decision
maker’s retrospective memory capabilities. These memories
are particularly susceptible to a slew of memory biases (e.g.,
misattribution, suggestibility, hindsight bias, fluency effects,
etc.). Caution should be given when interpreting results from
studies that use questionnaires to investigate clinical
decision-making (Aitken et al., 2011). To add to the problem,
questionnaire response rates in some studies drop as low as
29%, raising the issue of selective sampling bias (Thompson,

An additional method used to investigate nursing clinical
decision-making is through constructed interviews or focus
groups. These studies use an introspective approach to collect
data: An interviewer guides nurses to explain the
decision-making process and factors that affect it. The main
concern with all introspective approaches is that it capitalizes
on idiosyncrasies of the participant and the environment that
surrounds them. Generalizability is very limited, unless the
proper sampling techniques are used. For instance, factors that
impact novice nurses in one hospital setting might be unique
and not prevalent in other hospitals—a conclusion made by
Bucknall and Thomas (1995). In complex areas of study, such
as nursing, it is extremely challenging and very costly to
implement appropriate sampling techniques and still control
for nuisance variables.1

Setting aside the issue of sampling and generalizability,
introspective methodology is not necessarily an improper tool
for investigating nursing clinical decision-making factors. In
fact, can be an exceptionally powerful technique for grasping
a broad range of influential variables—it casts a wide net on
seemingly important factors. However, with any broad
research approach, additional studies (and to the extent
possible, experimentation) should be carried out to provide
corroborating evidence and rule out any idiosyncrasies.

Decision classification presents another difficulty in applied
clinical decision-making. What constitutes as a correct
decision? This issue is exacerbated by the fact that most
applied nursing research lack the feedback to ascertain
whether a nurse’s action plan reached an appropriate outcome.
For instance, most observational studies examine nurses for
several hours over a sequence of several days and observers
receive no feedback on the outcome of their nurse’s decisions
(Buckingham & Adams, 2000a; Long et al., 2007; Dowding &
Thompson, 2003). Furthermore, not all decisions or action

1 Stratified random sampling is not the be-all and end-all technique in
nursing research. Many authors argue that it is more important to get subjects
and data likely to generate robust, rich, and deep understanding (Thompson,

W. J. Muntean | Clinical decision-making 4

plans can be classified as binary. Decisions are often
considered on gradient scales. Take for example two decisions
or action plans that reach the same conclusion. Despite no
differences in outcome, the two decisions could differ in
efficiency, resources needed, complexity required, and
therefore ultimately differ in quality. One solution offered by
Bucknall (2000) and King and Clark (2002) is to encourage
researchers to conduct larger scale longitudinal studies. This is
an admirable request, indeed, but also a rather costly and
difficult paradigm to implement, hence only several studies
use this technique (Casey et al., 2004; Standing, 2007; O’Neill
& Dluhy, 1997).

Lastly, when comparing observational methods to think
aloud protocols, systematic differences have been observed.
Think aloud protocols have been shown to collect a greater
frequency of decisions than that of observation (Aiken et al.,
2011). Specifically, when investigating decision-making
involving assessment, diagnosis, and evaluation, think aloud
protocols should be used because it affords information that
cannot otherwise be collected by observation. However, there
are limitations with think aloud protocols. Nurses must be
comfortable with a continuous verbalization and they must be
given adequate practice sessions. In addition, the very nature
of thinking aloud might itself change the decision process that
occurs with covert thinking (e.g., Heisenberg effect and/or
Hawthorne effect; see Thompson, 2011). Observational
methods also have some limitations. They require the observer
to become a participant in the environment and their
interactions can influence the patient-nurse dynamics—the
consequence is creating an artificial setting (Luker & Kenrick,
1992). Therefore, the literature reviewed includes a mix of
both observational methods and think aloud protocols.

Before detailing each category of factors, I briefly describe
several frameworks of nursing decision-making that have been
endorsed throughout the literature. Although these frameworks
have been put forth primarily to distinguish between novice
and expert nurses, they are insightful and explain the core
elements involved in decision-making. Additionally, these
frameworks provide the context in which the contributing
factors are described in nursing research and, to some degree,
in the current review.


One source of complexity that surrounds nursing clinical

decision-making is that different nurses use different decision
strategies. Depending on the dynamics of the task a single
nurse can even use multiple strategies (Corcoran, 1986a,
1986b; Jenks, 1993; Cader, Campbell, & Watson, 2005).
Factors that influence one method of decision-making may not
have the same effect on another decision strategy (Baker,
1997). A unifying approach to nursing clinical
decision-making is exceptionally difficult for this reason.
There are a few proponents of this approach, though.

Buckingham & Adams (2000a, 2000b) suggest that the major
clinical decision-making theories are so similar that they only
differ in terminology and semantics. They argue that
decision-making research would be much more efficient and
communicable if the research community endorsed this
approach rather than placing so much energy on distinguishing
theories apart2. Despite the similarities (or differences) three
popular theories are summarized below.

Skills acquisition and the humanistic-intuitive approach

Perhaps the most influential framework of nursing
decision-making is Benner’s (1984) modification of the skills
acquisition theory (for a review, see Dreyfus & Dreyfus,
1986). Benner postulated that clinical decision-making
expertise is developed through experience as one progresses
through five stages of skill acquisition. The first stage is the
novice stage, which describes a beginner in the nursing
domain. They learn through instruction and learn
domain-specific facts, features, and actions (Gobet & Chassy,
2008). Novice decision-making is context free, meaning that
novices ignore idiosyncrasies of the situation. This results in
decision-making that is primarily rule based. It is inflexible
and resulting in very limited performance.

After acquiring a fair amount of experience, a novice
progress to an advance beginner. Advance beginners account
for more situational variables when making decisions.
Decision-making attributes start to become context dependent.
They also make use of limited past experience (given that they
have had a similar past encounter). The competence stage
involves organization structures such as hierarchical
long-range plans. Decisions are reached with greater
efficiency, albeit still relying on conscious, abstract,
analytical, and deliberate planning.

The proficiency stage marks holistic thinking rather than
fragmented subcomponents. Problem features are viewed as
salient or irrelevant, allowing decision-makers to organize and
analyze a situation intuitively, but analytical thinking is still
required to choose the action plan. Lastly, expertise stage
represents those who can understand a situation intuitively and
make decisions intuitively as well. Accordingly, experts act
naturally and often reach conclusions without explicit
understanding. Experts can revert to previous stages of
analytical thinking if a situation is novel or their initial
intuition is incorrect.

A strength of the humanistic-intuitive model of
decision-making is its simplicity. It describes the progression
from novice to expert succinctly—from a slow and hesitant
decision-maker to a fast and fluid problem solver. It captures
the relationship between knowledge and experience. Another
strength of the theory is that it captures the involvement of
emotion, namely in the intuition process (Benner et al., 1992;
Jenks, 1993). Perhaps this is the reason why the framework
has been adopted as the standard in nursing clinical
decision-making (Agan, 1987; Benner & Tanner, 1987;

2 For an example of the lively ongoing debate on nursing clinical
decision-making theories, see English, 1993; Darbyshire, 1994; Benner &
Tanner, 1987; Cash, 1995; Benner, 1996.

W. J. Muntean | Clinical decision-making 5

Corcoran, 1986a, 1986b; Crandall & Getchell-Reiter, 1993;
Pyles & Stern, 1983; Rew, 1988, 1990, 1991; Schraeder &
Fisher, 1986, 1987; Young, 1987). Intuition is
phenomenological in spirit and is often described as a feeling
of knowing something without conscious use of reason
(Banning, 2007) or an understanding without rationale
(Benner & Tanner, 1987). For this reason, hypothesis testing
is not necessarily used as a criterion for accurate or inaccurate
propositions and reasoning, which raised much skepticism as
to whether this approach is scientifically based (Banning,
2007; Cash, 1995; English, 1993).

Due to the phenomenological nature, researchers using this
approach have a difficult time unifying the definition of
intuition (Buckingham & Adams, 2000b). As a consequence,
nursing decision-making literature is filled with this loose
construct. For example, over 25% of the articles reviewed
used the term ‘gut feeling’ as a proxy for intuition when
surveying nurses on factors that led to their decisions (see,
e.g., Burman, Stepans, Jansa, & Steiner, 2002; Pretz & Folse,
2011; Ericsson, Whyte, & Ward, 2007). This raises the
question, how can this body of research differential between
‘gut feelings’ and guesses? If surveys included a guess option,
how would the endorsement of this choice be
interpreted—especially when a guess resulted in the correct
decision? Would that constitute as intuition, being a gut
feeling guess? Hence, therein lies the biggest criticism of this
framework, construct specificity (Rew, 2000).
Recent studies have made attempts to better define intuition
as it is used in nursing clinical decision-making (e.g., domain
specific intuition; Rew, 2000; Smith et al., 2004; Smith, 2006,
2007; Miller, 1995; Pretz & Folse, 2011). Rew (2000)
conducted a three-phase study on validating an intuition
assessment scale, hoping that it would provide a way to
measure a nurse’s propensity of utilizing intuition. The scale
started out with a 50-item questionnaire that covered six
conceptual categories relating to complex decision-making:
uses sudden/immediate insight, creativity, risk taking, rigidity,
cautiousness, and realistic approach (Rew, 1986; Masters &
Masters, 1989). An expert nursing panel reviewed the
assessment and reduced the number of questions to 28 items.
Following the review, a Content Validity Index was carried
out and revealed a high level of agreement (CVI = .96).

In the next phase, the assessment was sent out to 106 nurses
and responses were subjected to a principal component factor
analysis. This analysis led to a six-factor model. However,
seven questions had very low factor loadings and thus the
scale was reduced to 21-items. For the final phase of the study,
the reduced scale was administered to an additional set of
nurses. As before, a factor analysis was conducted on these
responses. This time three factors were retained, and only a
single factor clearly came from the original domain. The
author then further reduced scale to a unidimensional measure
of seven questions and labeled it as the Acknowledges Using
Intuition in Nursing Scale (AUINS) (see Table 1).
Interestingly, this measure has yet to be explicitly tested in the
decision-making literature. How does this measure correlate
with the quality and efficacy of decisions that nurses make?
Smith and colleagues have also made attempts at better
defining intuition (Smith, Thurkettle, & dela Cruz, 2004;
Smith, 2006). Using similar exploratory factor analyses that
Rew (2000) used, Smith et al. (2004) developed their own
intuition measurement scale. This resulted in a 25-item scale
with seven factors: physical sensations, premonitions, spiritual
connections, reading cues, sensing energy, apprehension, and
reassuring feelings (see Table 2). The most striking issue with
this study (and the follow-up study, Smith, 2006) was that
nursing students were used as participants. This is seemingly
problematic because according to Benner’s theory, novice
nurses lack the intuition abilities that expert nurses have
(Benner et al., 1992). Collectively, these studies take on the



1 There are times when I suddenly know what to do for a
patient, but I don’t know why.

2 I am inclined to make decisions based on a sudden flash of

3 There are times when I immediately understand what to do
for a patient, but I can’t explain it to other people.

4 There are times when I feel that I know what will happen to
a patient, but I don’t know why.

5 There are times when a decision about my patient’s care just
comes to me.

6 There are some things I suddenly know to be true about
some of my patients, but I am unable to support this with
concrete data.

7 Sometimes I act on a sudden knowledge about a patient to
prevent a crisis from developing even when I can’t explain

Note—Reproduced from Rew (2000)




I get a shiver down my spine when I think something is
wrong with my patient.

The hair on my arms and neck stand up when something
is wrong with my patient.

I get a lump in my throat when something is wrong with
my patient.

I feel cold when something is wrong with my patient.
I feel nauseous when something is wrong.


I experience a gut reaction when something is wrong
with my patient.

I get a bad feeling about a patient’s condition.
I get a persistent feeling about a patient’s condition.
I get a sinking feeling in my stomach when something is

about to go wrong.


I connect with my patients at the soul level.
I sense a spiritual connection with my patients.
I experience a deep connection with my patients.

I do not need verbal communication to sense a spiritual
connection with my patient.

Reading cues

I read the non-verbal body language of my patient.
I read non-verbal cues of my patient.

I can read my patient’s expressions.

I sense positive energy coming from my patient.
I sense negative energy coming from my patient.

I sense an energy field around my patient
Apprehension I experience a feeling of dread when caring for my

I get a nagging feeling about a patient’s condition.
I feel anxious when I think something will go wrong.
I get an odd feeling about a patient’s condition.


I get a calm feeling when I know things will be okay.
I get a peaceful feeling when I know my patient is

Note—Reproduced from Smith, Thurkettle, & dela Cruz (2004)

W. J. Muntean | Clinical decision-making 6

challenge of establishing a valid construct of intuition, but
there are too many remaining issues surrounding the
measurement scales that prevent their adoption in the literature
(but see Pretz & Folse, 2011).

Despite the aforementioned challenges with conducting
research on domain specific intuition, there is plenty of
evidence that suggest the role of intuition in nursing clinical
decision-making. Pretz & Folse (2011) administered several
domain specific measures of intuition, as well as domain
general (e.g., Myers–Briggs Type Indicator, Myers,
McCaulley, Quenk, & Hammer, 1998; Rational-Experiential
Inventory, Pacini & Epstein, 1999), to nurses of various
experience (from nursing students through nurses with over 25
years of experience). The researchers sought to test the
hypothesis that preference and the use of intuition increased
with experience. Their battery of tests and surveys showed an
overwhelming use of intuition and more experienced nurses
placed a greater reliance on intuition when making clinical
decisions, confirming their hypothesis. Additionally, King and
Clark (2002) conducted an observational study on nurses
ranging from advance beginner to expert nurses (according to
Benner’s classification; Benner & Tanner, 1987) and found
traces of intuition in both expert and non-expert nurses, but
intuition was better utilized by expert nurses. These studies,
along with others (King & Appleton, 1997; Manias, Aitken, &
Dunning, 2004; McCormack, 1993; Traynor, Boland, & Buus,
2010) suggest that experience and expertise are key factors in
the use of intuition.

Information-processing Model and
Hypothetico-deductive Reasoning

An alternative framework for nursing clinical

decision-making is the information-processing model. The
decision-maker is viewed as a capacity-limited
information-processing system that interacts with the problem
task (Joseph & Patel, 1990; Hamers, Abu Saad, & Halfens,
1994). It assumes the system (i.e., the decision-maker) has a
memory component comprising two parts, short-term and
long-term memory. In simple terms, short-term memory holds
information for online processing. It is limited in capacity,
usually no more than seven chunks (e.g., recognizable
patterns; Newell & Simon, 1972). Long-term memory is
limitless and stores knowledge structures of both factual
(semantic) and experiential (episodic) memories. The
interaction of short-term and long-term memory provides the
mechanism of information processing used in
decision-making. This model attributes decision-making to
four components: (a) cue acquisition, (b) hypothesis
generation, (c) cue interpretation, and (4) hypothesis
evaluation (Elstein, 1976).

Initially, a decision-maker searches for cues relating to the
problem task. Data collection occurs through various methods,
sometimes even implicitly. For instance, a nurse might read a
patient’s medical chart—an explicit method of data
collection—and then supplement that information with salient

cues gained from their interaction with the patient—implicit
cues. Afterwards, or in some cases even before finishing data
collection, a hypothesis is generated from long-term memory.
This is followed by the generation of an action plan.

The framework places a constraint on the number of
working hypotheses a decision-maker can hold active in
short-term memory, usually four or five. Hypotheses and
action plans are loosely tested through cue evaluations, where
cues are considered as either supporting, refuting, or
noncontributing to the hypothesis. Finally, the hypothesis is
evaluated and either entertained or replaced by another
generated hypothesis, thereby repeating the evaluation
process. This framework is assumed to be analytical, arriving
to a conclusion in a logical and linear manner that can be
repeated and readily communicated (Panniers & Walker,

The information-processing model uses a scientific or
hypothetico-deductive approach to assist metacognitive
reasoning (Banning, 2007). It makes the assumption that
decision-makers follow rational logical when arriving to a
decision. Much like the humanistic-intuitive approach, the
analytical model also accounts for different levels of
experience and knowledge. During the cue recognition stage,
past experience facilitates the recognition of meaningful cues
and patterns; greater experience results in faster processing
and finer recognition acuity of relevant cues. Hence, less
experienced nurses will find it more difficult to initially
understand a problem-task than a nurse with more experience
(O’Neill, Dluhy, & Chin, 2005).

In addition, a novice nurse’s lack of experience will hinder
the hypothesis generation phase—they will have greater
difficulty producing an appropriate hypothesis. This, coupled
with the lack of cue recognition, produces a synergistic effect
of inaccurate decision-making. Not only can the
information-processing model account for differences in
experience, it has an advantage over the humanistic-intuitive
approach by providing a template to facilitate communication
on how one arrived to a conclusion. This is a key strength of
the analytical model and an element that distinguishes itself
from the intuitive approach of decision-making: Intuition is an
intangible process, whereas hypothetico-deductive reasoning
follows well-defined stages. Moreover, the model breaks
down complex decision-making into simpler parts that can be
carefully examined in isolation.

Support for the information-processing model comes from
the large body of studies comparing novice and expert nurses
(see, e.g., Boblin, Baxter, Alvarado, Baumann, &
Akhtar-Danesh, 2008; Botti & Reeve, 2003; Lamond et al.,
1996). In one study, Offredy (1998) conducted structured
interviews, taped interviews, and finally observations of
practitioner consultations. Interview transcriptions and
observation notes were subjected to a content analysis and
categorized according to which type of decision process was
used. Offredy noticed that for anything more than simple
low-consequence routine issues, nurses began using
hypothetico-deductive reasoning. This was true for all levels
of expertise, though Offredy pointed out that experienced

W. J. Muntean | Clinical decision-making 7

nurses did report the involvement of intuition. Interestingly,
when task-problems became complex and unfamiliar, expert
nurses abandoned the use of intuition and reverted back to
analytical models of decision-making. This study, like others
(e.g., Hicks et al., 2003; Hughes & Young, 1990), shows the
involvement of both analytical reasoning and intuition in
nursing clinical decision-making.

Cognitive Continuum Theory

Analytical decision-making and intuition are not mutually

exclusive, per se. While analytical reasoning follows
procedural rules to reach a decision, intuition is not occluded
from the involvement in this process. Besides, both strategies
involve pattern/cue recognition. According to the
hypothetico-deductive model, cue recognition primarily
involves conscious recognition, whereas intuition is
exclusively a subconscious recognition (Manias et al., 2004).
Similarly, hypothesis generation under the
hypothetico-deductive model is explicitly formulated from the
reviewed data, whereas intuition assumes hypotheses are
generated implicitly with a degree of automaticity
(Buckingham & Adams, 2000b). Several theories describe the
decision-maker’s transition from analytical decision-making to
more abstract intuitive strategies (see, e.g., Cader et al., 2005;
Standing, 2008; Harbison, 2001).

The cognitive continuum theory serves to reconcile the
opposing views that decision-making is purely analytical or
purely intuitive (Hammond, 1981; Harbison, 2001). It assumes
that decision-making strategies are located along a continuum
that is determined by the task structure (Hamm, 1988;
Thompson, 1999b). In other words, the situational context of a
problem determines which strategy is the most ideal approach
to decision-making. In nursing clinical decision-making
research, evidence for this postulation comes from studies that
show nurses varying decision strategies for different tasks and
problems (Corcoran, 1986a, 1986b; Hicks et al., 2003; Lauri et
al., 2001; Cader et al., 2005; Hughes & Young, 1990). In
addition, many nursing decision-making theorists proclaim the
importance of contextual elements, also commonly known as
domain-specific knowledge structures (Crow, Chase, and
Lamond, 1995).

In a study conducted by Crow et al. (1995), nurses reported
that experience in a particular nursing domain brought about a
contextual familiarity. In familiar situations, elements
surrounding the task problem are more concrete and easily
understood. By contrast, unfamiliar situations bring about
ambiguous task elements and are more challenging to discern.

In the cognitive continuum, familiarity is subsumed by how
ill- or well-structured a task is perceived by the
decision-maker. The amount and type of information cues
associated with the judgment task is critical to the theory. A
problem increases in structure as more cues are recognized,
provoking the use of analytical decision-making. As a problem
becomes more ill-structured the theory suggests the
decision-maker should increase their preference towards

intuitive decision-making. On the one hand, well-structured
tasks can be compartmentalized, have a high degree of
certainty, and are not marked by time constraints. On the other
hand, ill-structured tasks have a low-level of decomposition,
have a high degree of uncertainty, and have to be resolved
quickly (see Figure 1).

Additional factors help determine where along the cognitive
continuum a task problem lies. According to the theory, task
complexity—which can involve the number of information
cues, redundancy of cues, or the principle of combining
cues—has a major impact on decision-making strategies;
which is empirically supported in several studies (Corcoran,
1986a, 1986b; Lewis, 1997; Hughes & Young, 1990).

The environmental presentation of material related to the
problem has a similar effect; it can either reduce or increase
the structure of the decision-task. If the environment allows
for an adequate time to reach a decision then the perceived
structure of the task is increased. The opposite is true as well:
Greater time pressure results in less structured tasks and
requires a greater reliance on intuition. In contrast to holistic
representations of the decision task, the environment can
present decision information in smaller
subcomponents—allowing the decision maker to analyze the
information linearly and independently. As mentioned above,
this results in well-structured tasks.

In summary, the cognitive continuum theory brings
resolution to the opposing views that decisions are either all
analytical or all intuitive. It balances the criticism that (a)
analytical thinking is a unitary generic process that is
insensitive to idiosyncrasies of the decision task context and
(b) that intuition is almost entirely tied to context-specific
elements that are unique to each decision. This solution
acknowledges the diversity of individual cognitive strategy
thereby mitigating some complexity in nursing
decision-making. It provides a valid framework for theoretical
and empirical testing, some of which will be discussed below.

Fig. 1. The cognitive continuum. Reproduced from Lamond & Thompson

W. J. Muntean | Clinical decision-making 8


The literature review revealed studies mentioning a number

of factors that contribute to clinical decision-making. A
variety of methods were used in these studies; controlled
experimental settings, robust observations, focus group
interviews, and/or questionnaires. As such, the scientific rigor
varies between studies, resulting in several inconsistent
findings. However, despite the lack of scientific merit of some
studies, there are “clusters of recurring findings” (Thompson,
1999a pg. 816) that suggest these factors be given some a
priori theoretical consideration. The following factors are
categorized as either individual factors or environmental

Individual Factors

Age and Education Level

Bakalis, Bowman, and Porock (2003) conducted an
experiment comparing Greek and English nurses on their
clinical decision-making abilities. Within each country, eight
hospitals were randomly selected to sample nurse volunteers.
Nurses from coronary care units with a minimum of 6-months
experience participated. Eight clinical decision-making-cards
were presented to the nurses; half on acute phases and the
other half on recovery phases. Each card required at least five
decisions to be made, which were scored on a 5-point scale
ranging from helpful to inappropriate. Furthermore, the nurses
had a “call the doctor” option to indicate where in the
decision-making process the nurse handed the decision over to
the medical staff. In addition to the clinical
decision-making-cards, nurses had to rank order 10 factors
(knowledge, clinical experience, job description, intuition,
medical cover, clinical guidelines, authority, autonomy, stress,
and post-registration education) on their importance in
influencing their decision-making.

Collapsing across country, the authors separately regressed
decision-making scores on four demographic variables—age
of nurse, years of experience, academic attainment, and
medical cover. All four factors had reliable effects on decision
scores; nurses scored higher with increasing age, with more
experience, with more academic attainment, and with more
medical cover. It is important to point out that there were
significant correlations among the demographic variables, so it
is unclear from the analyses run whether the variables measure
a similar underlying construct—knowledge. Nonetheless, this
study underscores the importance that knowledge plays in

The Bakalis et al. (2003) experiment supported the
hypothesis that academic attainment positively impacts
clinical decision-making, but the literature review revealed
conflicting results. Some studies showed that education level

promoted successful decision-making (Prescott et al., 1987;
Verhonick, Nichols, Glor, & McCarthy, 1968; Davis, 1974;
del Bueno, 1983; Shin, 1998; Girot, 2000), while other studies
found no effect or even a negative effect (Pardue, 1987;
Frederickson & Mayer, 1977; Mayer, 1975; Sanford et al.,
1992; Sims & Fought, 1989; Bechtel et al., 1993; Hicks et al.,
2003; Henry, 1991; Lauri & Salantera, 1995; Twycross &
Powls, 2006). These disparate findings suggest that other
factors related to experience and knowledge might play a
larger role than merely education level alone.

Verhonick et al. (1968) showed nurses a filmed patient
scenario and then had them fill out questions pertaining to
observations and action plans. Nurses with higher levels of
education were more observant to cues provided by the
patients and in turn made better action plans. Although this
study did not directly measure decision-making per se, it
showed that nurses with higher levels of education were able
to identify more cues and evaluate them properly. In addition,
the authors reported nurses with less than 1-year experience
were the worst in selecting action plans, owing to their poor
observational and cue recognition skills.

Frederickson and Mayer (1977), Mayer (1975), and Davis
(1974) made attempts to replicate the previous results using
the same filmed scenarios. Frederickson and Mayer (1974)
and Mayer (1975) found no differences in performance as a
function of academic attainment or experience. One
methodological difference was that they used think-aloud
procedures. These researchers did report that baccalaureate
nurses scored higher on the Watson-Glaser Critical Thinking
Appraisal than did nurses who held only a high school

By contrast, Davis (1974) did replicate the major finding of
Verhonick et al. (1968) but also reported a result that was
troubling to explain. Nurses performed better as education
levels increased, as predicted, but when analyzing responses
as a function of clinical experience, nurses started to perform
worse after six years of experience. Davis then evaluated
nurses who took refresher courses and concluded that as long
as nurses took refresher courses experience predicted
performance scores.

Lauri and Salanterà (1995) also investigated time since last
professional training or reorientation. They constructed a
questionnaire to investigate the propensity for nurses to use
intuitive decision-making strategies and
information-processing strategies. The intuitive questions
reflected nursing knowledge, practical experience, and nursing
context, while the information-processing questions reflected
data collection, data processing, plans of action, and
monitoring and evaluation. Two hundred Finnish nurses
responded to the questionnaire and these data were subjected
to a factor analysis. A four-factor solution was retained: Factor
1 represented the use of questions during the data collection
process; Factor 2 assessed creative decision-making; Factor 3
assessed whether nurses were patient-oriented or
nursing-oriented; and Factor 4 represented the likelihood of
using rule-based decisions.

Factor scores were calculated for each participant and then

W. J. Muntean | Clinical decision-making 9

analyzed as a function of experience, time since last
professional training, and knowledge structure—which was
determined by a content analysis from open-ended questions
and classified as either abstract or concrete. Nurses with less
than two years experience used a questioning approach to
collect patient data and nurses with 3-5 years experience used
an “unquestioning” approach. That is, they collected patient
information more or less through observations. Nurses who
had not received professional training or reorientation in ten or
more years tended to be patient-oriented and were able to
observe more patient related cues. Knowledge structure was
significantly correlated with creative decision-making; nurses
with more abstract knowledge structures had higher creative
decision-making scores.

Sanford et al. (1992) reanalyzed data collected by a nursing
education department during a hospital orientation of newly
graduated nurses. Much like the previously mentioned studies,
the authors were interested in the effects of education level on
decision-making abilities. Of the 116 nurses analyzed, 112
graduated within a year and 74 graduated with a baccalaureate
degree in nursing. Each nurse watched four video vignettes
and had to (a) identify the specific patient problem, (b) specify
the nursing interventions required in order of priority, (c)
identify the rationale for each stated intervention and (d)
identify preventive actions that could have eliminated or
minimized patient risk. Responses were scored on a
three-point scale ranging from a completely acceptable answer
to a completely unacceptable answer. These data revealed no
reliable difference between nurses with or without a
baccalaureate degree.

The effect of education level on decision-making is, at best,
inconclusive. One potential explanation is that studies were
using a coarse measure of education and therefore were
insensitive in detecting differences. For example, most studies
contrasted high school diploma against all other levels of
education. Perhaps other measures of education might provide
some resolution on the conflicting results.

Experience, Knowledge, and Cue Recognition

Experience, knowledge, and cue recognition are all
intimately related. Cue recognition depends on knowledge,
which is gained through years of experience. On the surface,
this causal chain seems plausible and convincing. In fact, it is
a pillar of the skills acquisition theory (i.e., the
humanistic-intuitive approach to decision-making) and is
implicitly represented in the information-processing theory.
Despite the theoretical merit, this causal chain has not yet been
directly tested empirically. There is, however, evidence
demonstrating the importance of each of these factors on

Studies that have enough subjects often use
years-of-experience as a covariate (see, e.g., Bakalis et al.,
2003; Lauri & Salanterà, 1995) and typically find a small
effect or no effect at all (Lauri & Salantera, 1998). When there
are fewer subjects, years-of-experience is either neglected or
coarsely clustered and analyzed in a descriptive manner (see,

e.g., Twycross & Powls, 2006; Monterosso et al., 2005;
Tanner et al., 1987; Henry, 1991). Nursing clinical
decision-making research that is interested in experience place
primary focus on decision strategies rather than efficacy of
decisions. When nurses of different experience use the same
strategies, research concluded that they are of the same
expertise (Twycross & Powls, 2006). This makes it difficult to
disentangle experience and expertise—especially since
years-of-experience has been a measure of expertise (Benner
& Tanner, 1987; Benner, 1984; Grobe et al., 1991; Benner et
al., 1992; Hedberg & Larsson, 2004).

In one study, Scottish nurses were presented with patient
scenarios and were instructed to provide decision-making
information using a think-aloud protocol (Twycross & Powls,
2006). Transcriptions were coded and categorized according
to data collection, data interpretation, action plans, and
evaluation. Interestingly, no differences were found between
nurses with more than five years experience (which is
commonly used as a threshold to classify nurses as experts;
see Benner, 1984) and nurses with less years of experience.
Furthermore, regardless of experience, all nurses used very
similar backward-reasoning decision strategies, which is an
indication of novice decision-making. Twycross and Powls
concluded that nurses in their study were all of equal

Years-of-experience has also been shown to have no
bearing on clinical decision-making frequency. Hoffman,
Donoghue, and Duffield (2004) surveyed roughly 100
Australian nurses to investigate factors that contributed to
perceptive and normative decision-making. Perceptive
decision-making was defined as decisions that nurses believed
they made, whereas normative decision-making was defined
as decisions that nurses wanted to make. Essentially, Hoffman
and colleagues were interested in the factors that influenced
decision-making propensity. They found that age had an
impact on perceptive decision-making; increases in age were
accompanied with increases in perceived clinical
decision-making. Interestingly, they found no similar effect
for years-of-experience or education level—which were both
significantly correlated with age.

It is puzzling to find conflicting studies on the effects of
nursing experience—at least measured by years. All major
theories of clinical decision-making rest on the assumption
that experience is a major determinant of competent
decision-making. Past experience brings about a familiarity of
the elements involved with the decision task at hand.
Therefore, years-of-experience should result in more fluent
decision-making. While several studies demonstrated this
effect either statistically or descriptively (Benner & Tanner,
1987; Benner, 1984; Grobe et al., 1991; Benner et al., 1992;
Hedberg & Larsson, 2004; Watson, 1994; Lauri & Salantera,
1995; Wainwright et al., 2011; Thompson et al., 2008;
Monterosso et al., 2005; Westfall, Tanner, Putzier, & Padrick,
1986) it was not always obtained (Twycross & Powls, 2006;
Tanner et al., 1987; Henry, 1991; Lauri & Salantera, 1998;
Greenwood & King, 1995; Corcoran-Perry & Cochrane,
1999). Perhaps it is safe to conclude that not all experience is

W. J. Muntean | Clinical decision-making 10

One explanation offered for the discrepant results is that

past experience can actually lead to systematic biases
(Thompson, 1999a; Tanner & Hughes, 1984). Nurses are
better able to generate and consider more hypotheses as they
gain experience. However, as a byproduct, nurses can over
sample recent experiences and neglect older, but still useful,
experiences. Furthermore, nurses assess probabilities of the
associations between cues and likely outcomes when
interpreting cues—which is biased by past experience
(Kahneman & Tversky, 1996). Dramatic and profound events
come to mind more easily and cause additional interference in
assessing accurate probabilities. Inaccurate probabilities lead
nurses to make inadequate decisions; hence, inaccurate
probabilities are a counteracting force to experience.

Although that might explain one potential drawback of
nursing experience, there are still many benefits (see
Thompson, 1999a). Experience is associated with greater
pattern recognition in the hypothesis generation stage of the
information-processing strategy (Draper, 1986). It allows for
more complex combinations of chunks in short-term memory
(Gobet & Chassy, 2008), making it easier to access related
information in long-term memory. In addition, nurses with
greater experience can activate more complex hypotheses—a
major benefit in difficult decision-tasks (Westfall et al., 1986).
Therefore, experience helps develop other decision-making
facilitators, such as knowledge and cue recognition (Bakalis et
al., 2003; Baumann & Bourbonnais, 1982; Benner & Tanner,
1987; Abu Saad & Hamers, 1997; Bucknall & Thomas, 1997;
Caputo & Mior, 1998).

Casey et al. (2004) conducted a longitudinal study of newly
graduated nurses in the Denver metropolitan area. The authors
were interested in factors impacting the transition into a
registered nurse. Surveys were distributed to nurses with
approximately a year or less of experience and newly
graduated nurses (nurses with less than 3 months experience)
were surveyed once more. The questionnaire had a mixture of
multiple-choice and open-ended questions from five
categories: demographics, skills/procedure performance,
comfort/confidence, job-satisfaction, and difficulties in role
transition. One of the major themes that the newly graduated
nurses expressed was their lack of knowledge, which affected
their ability to make decisions while caring for their patients.
Only nurses who approached a year of experience began to
express that they finally started being comfortable with their
level of knowledge—indicating, at least to some degree, that
experience played a role in knowledge acquisition.

In a related study, Ebright et al. (2004) conducted
semi-structured interviews with twelve novice nurses that had
at least three months experience but no more than a year
experience. The authors were investigating factors that
contributed to near-miss/adverse-event situations. Nearly all
cases of reported events were due to lack of knowledge base
related to the decision-task. Novice nurses often found
themselves in so-called first-time situations, where they lacked
knowledge and experience. Lack of knowledge impeded
competent decision-making and resulted in near-miss or

adverse events.
Poor decision-making due to lack of knowledge is not just

limited to novice nurses, however. Knowledge is a core
attribute to decision-making and can impact nurses of all
levels of expertise and experience. Bucknall and Thomas
(1997) surveyed Australian critical care nurses on issues
related to clinical decision-making. Unlike the nurses used in
Ebright et al. (2004), these nurses had an average of nine years
of clinical experience. The researchers asked nurses to indicate
how often they experienced problems in making decisions
because of their knowledge base. The responses were
surprising: More than 1 out 3 nurses indicated that their
knowledge base posed a problem in making decisions on a
weekly basis. Therefore, regardless of clinical experience,
knowledge is the fuel that makes decision-making effective.

Cue recognition also depends on knowledge base (Manias
et al, 2004; Hedberg & Larsson, 2003). Nurses making
decisions must distinguish important information from
irrelevant cues (Corcoran, 1986a, 1986b; Thiele, Holloway,
Murphy, & Pendarvis, 1991; Thiele et al., 1986). And as
Tanner et al. (1987) indicated, the majority of novice nurses
use an analytical hypothesis-driven decision-making approach
that relies on cue recognition. Cue and pattern recognition are
not just limited to novice nurses, though. It is implicitly
embedded in intuition—which is a marker of expertise—and
has been theorized to be unconscious cue recognition
(Offredy, 1998). Under ambiguous and uncertain situations,
like many found in clinical nursing, dismissing cues as
irrelevant and relying on only partial cue information can lead
to inappropriate decision-making (Girot, 2000; Corcoran,

A specific kind of cue recognition is identifying diagnostic
cues—ones that rule out opposing interventions and action
plans. Students who were able to identify and make use of
these cues made better clinical decisions (Elstein, 1978).
However, students in general have a difficult time recognizing
cues and distinguishing relevant from irrelevant information
(Thiele et al., 1986, 1991). Novice nurses might not be that
much better, though.

Itano (1989) used nurse-patient observations and discovered
that expert nurses collected more cues than did novice nurses,
which has been observed by other researchers (Tanner et al.,
1987; Taylor, 1997; Hoffman et al., 2009). Hoffman and
colleagues replicated this finding using Australian Intensive
Care Unit novice and expert nurses; with novice nurses having
fewer than two years of experience. Data was collected
through a think-aloud protocol with actual patients. Compared
to expert nurses, novices collected nearly half as many cues.

Additionally, novice nurses clustered cues in a linear
manner, demonstrating simple organization structures. Expert
nurses, by contrast, clustered cues in complex schemas,
allowing them to consider more information in parallel.
Novices also engaged in less proactive cue collection (i.e.,
planning ahead, anticipating what would happen, and
collecting cues in anticipation of problems) and instead were
more focused on retroactive tasks. That is, novices waited for
a problem to occur and then collect cues in response to the

W. J. Muntean | Clinical decision-making 11

problem. Preventative cue collection seems to play a large role
in decision-making, but more research is required on the topic
(Hoffman et al., 2009).

In contrast to the previous studies, Greenwood and King
(1995) found that novice nurses actually collected more cues
than did expert nurses. However, they attributed this finding to
an inability to discriminate between salient and non-salient
cues. Novices simply collected more cues regardless of
whether the cues would be helpful or not. Despite the
importance of cue recognition in decision-making there is a
lack of research using novice nurses; most studies rely on
experts or students as participants.

In a study using senior baccalaureate nursing students,
Thiele et al. (1986) demonstrated the impact of cue
recognition on decision-making. The experiment used a
pre-test/post-test design with each test presenting new clinical
situations that required participants to identify and sort cues,
as well as link them together to make decisions. In between
tests, the students engaged in computer-assisted learning
simulations. They were presented information on effective
decision-making and cue recognition. Although the
experiment was not conducted on registered nurses, several of
the experiment’s conclusions are relevant for novice nurses.

First, the pre-test showed that participants were identifying
nearly as many irrelevant cues as relevant ones. It should be
no surprise, then, that the students reached many inappropriate
decisions. According to the study, students are not readily
provided with decision-making training and are not taught the
importance of cue recognition. Extrapolating this logic to
novice nurses, if their ability to recognize cues is
substandard—compared to nurses with more experience—then
it will likely contribute to decision-making errors. Second, the
post-test indicated that, after completing the computer
simulations, senior students were significantly better able to
differentiate between relevant and irrelevant cues. Moreover,
their decision-making scores reflected this improvement; they
made better and more appropriate decisions. And finally, the
authors noted that once participants began improving their cue
recognition they were able to chunk the cues together and link
them in meaningful ways that assisted their decisions.
Accordingly, chunking cues allows more information to be
considered simultaneously, which facilitated the evaluation of
decisions and hypotheses considered. These results show that
novice nurses may require some training to promote
successful decision-making through cue recognition.

While cue recognition is considered a necessary component
of accurate decision-making, it does have some drawbacks.
Radwin (1995) studied nurses’ decision-making in a 30-bed
cardiology unit. After analyzing field notes and
post-observation interviews, Radwin described nurses as
having several decision strategies. When time was not
constrained many nurses engaged in what Radwin termed,
knowing the patient. Essentially, it is a purposeful action to
understand the patient’s experiences, behaviors, feelings,
and/or perceptions to select individualized interventions.
Radwin reasoned that knowing the patient allows for more
personalized care and decisions. Under certain circumstances,

such as when time is constrained or under high pressure,
nurses do not have the resources to implement this strategy.
Instead, nurses switched to a pattern and cue recognition
strategy where decisions and interventions were selected based
on familiarity of previous patients.

Radwin argued that cue recognition and using familiarity of
previous situations to determine interventions ultimately
reduces patients to symbols and patterns—moving away from
individualized care (Radwin, 1995; 1998). However, this
heuristic is effective and efficient (Buckingham & Adams,
2000b; Woolley, 1990; O’Neill, 1995). Furthermore, pattern
recognition and individualized care are not mutually
exclusive. In fact, individualization can be facilitated when
nurses are able to recognize a greater variety of patterns and
cues (O’Neill, 1995; Smith, Higgs, & Ellis, 2008; May, 1996).

Hypothesis Updating

Cue recognition contributes to clinical decision-making
mainly by allowing nurses to generate a working hypothesis
and evaluate decisions or action plans (Greenwood, 2000;
Rawdin, 1990; Lewis, 1997). In addition, they can be used to
update hypotheses or generate entirely new ones (Thompson,
1999b). In fact, updating hypothesis is an essential component
of the information-processing model of decision-making.
Often times a nurse’s initial hypothesis is not in the best form
and must be modified when receiving new information.
Failing to do so can result in errant decisions and contribute to
poor decision-making (Manias et al., 2004).

Updating and revising hypotheses have been studied using
various methods, but the most popular approach is comparing
nursing decisions to probabilistic models. In this paradigm,
nurses are required to make decisions (e.g., on the current state
of a patient or to specify an appropriate action plan) in a
sequential manner, typically after new patient information is
revealed to the nurse. Additionally, the nurses accompany
their decisions with likelihood estimations that their decisions
are correct. At each step, the nurse’s decisions are compared
with a normative model and then assessed on their ability to
update their hypothesis (Hughes & Young, 1990; Dowding &
Thompson, 2003; Cioffi, 2011).

Hammond, Kelly, Schneider, and Vancini (1967) conducted
one of the first experiments pertaining to hypothesis
revaluation and updating. Six nurses were presented with four
patient scenarios and instructed to select as many as 15 cues
from the 128 cues available to them—stopping when the
nurses felt that additional cues would not change their
decisions. Following each selection, the nurses had to
determine the state of the patient, which was then compared to
a Bayesian normative model. The main finding in this study
was that nurses were overly conservative when updating their
hypothesis—they were cautious in changing their original
hypothesis. On the average, nurses changed their likelihood
estimations at a third of what the normative model predicted.
The authors warned that such caution or reluctance to update
the working hypothesis could contribute to decision errors.

The conclusions made by Hammond et al. (1967) are

W. J. Muntean | Clinical decision-making 12

corroborated by other studies (Corcoran, 1986a, 1986b;
Ebright et al., 2004; Ramezani-Badr, Nasrabadi, Yekta, &
Taleghani, 2009). When discussing factors that led to adverse
events, Ebright et al. (2004) noted that novice nurses too often
“loose the big picture” and ignore new aspects of a patient’s
condition. Essentially, nurses were not able to update their
hypothesis when presented with additional information.

In complex decision tasks, novice nurses were described as
taking too narrow of an approach, placing a limit on their
abilities to update their hypothesis (Corcoran, 1986a). By
contrast, expert nurses took a broader initial approach and then
refined their hypothesis accordingly. Furthermore, Corcoran
(1986a) reported that a source of erroneous decision-making
was the inability to combine patient information with an
alternative hypothesis (e.g., hypothesis updating). This issue is
exacerbated in complex tasks: Corcoran noted that fewer
alternative hypotheses were being evaluated, despite more
being generated. Although this was attributed to a limited
short-term memory capacity, it is important to mention that
evaluation of hypotheses also plays a critical role in

The quality and complexity of hypothesis generation has
also been investigated (Westfall et al., 1986; Tanner et al.,
1987). Westfall et al. showed nursing students and RN nurses
several videotaped patient scenarios and analyzed the
participants’ verbal protocols. Each scenario had several
accurately diagnostic hypotheses as well as several plausible
but inaccurate hypotheses; hence, participants could produce
and update multiple hypotheses. All hypotheses generated
were scored and used to create several measures.

Complexity was judged by the link between cues and
hypothesis. A direct link between a cue and a hypothesis was a
relatively simple hypothesis because it naturally led to the
hypothesis. By contrast, an indirect link required more
information than a cue could provide. A participant must hold
a working hypothesis in memory and update it accordingly
when receiving additional information. Therefore, hypotheses
generated from indirect links were judged as more complex. A
complexity scored was the ratio of the number of indirect
hypotheses generated to all hypothesis generated. In addition,
hypotheses were judged on comprehensiveness (whether
nurses were able to generated all potentially acceptable
hypotheses), efficiency (a ratio of acceptable hypotheses to all
hypotheses generated), proficiency (a ratio of acceptable and
plausible but inaccurate hypotheses to all hypotheses
generated), and earliness (the proportion of hypotheses
generated during the first half of the verbal protocol

Oddly enough, nurses performed no better than students on
the comprehensiveness measure—all groups produced around
20% of the acceptable hypotheses. The low proportion of
accurate hypotheses brings to question whether the materials
used (e.g., scenarios and cues provided) were sensitive enough
to study the decision-making process. Regardless, students and
nurses produced equal scores of efficiency, proficiency, and
earliness, but differed in complexity. Nurses generated
hypotheses that were more complex and required updating

with additional information.
The same authors published a related study investigating

hypothesis generation and decision-making (Tanner et al.,
1987). Using what seems like the same set of subjects, the
authors conducted a similar experiment but analyzed the
verbal reports slightly differently, adding several new
measures. One new measure bears mentioning. Participants
were allowed to ask questions to receive additional
information on the patient. These questions were scored on
their relevancy to hypotheses generated. The authors described
this as hypothesis-driven questions for the purpose of updating
and modifying working hypotheses. Nurses asked more
relevant questions than did students and this was taken as
evidence that nurses were able to update their hypotheses.
Despite the more relevant questions asked, all groups—nurses
and students, alike—produced an equal amount of accurate
hypotheses. Therefore, these results should be interpreted with


Novice nurses encounter unfamiliar situations on a regular
basis during their first year of nursing (Ebright et al., 2004;
Smith et al., 2008; Casey et al., 2004). As discussed earlier,
novices that lack domain-specific knowledge are more prone
to make decision errors (Bucknall & Thompson, 1997;
Watson, 1994; Baumann & Bourbonnais, 1982; Benner &
Tanner, 1987; Abu Saad & Hamers, 1997; Corcoran, 1986a,
1986b). When novices are faced with these unfamiliar
conditions they have the opportunity to receive assistance
from more experienced colleagues. Whether or not novices
exercise this option has been determined to influence
decision-making (Jenks, 1993; Manias et al., 2004; Hedberg &
Larsson, 2003).

One cannot help but wonder whether a novice’s propensity
to communicate with colleagues, for the purpose of receiving
assistance or guidance, has been a reason for so many
discrepant results in applied nursing decision-making research.
In other words, communication willingness, and ability, is a
significant covariate in nursing decision-making
research—hence, a confound when not controlled for. This
underscores the complexity that surrounds applied research on
nursing clinical decision-making.

Hedberg and Larsson (2003) observed experienced nurses
and described a central theme that was abundant in clinical
decision-making—the corroboration of information with
colleagues. Nurses approached and consulted each other on
cues and information gathered from patients. They asked
whether other nurses had any experience with their patients or
had encountered patients with similar conditions. According to
the study, nurses used corroboration for the purpose of
minimizing the risk of making a wrong decision. Written
communication was less effective than was face-to-face
communication, especially under ambiguous situations where
cue interpretation was difficult.

Using a focus group technique, Jenks (1993) discovered that
clinical decision-making was facilitated through knowing the

W. J. Muntean | Clinical decision-making 13

patient, the peer nursing staff, and the physicians. Much like
what Radwin (1995) termed knowing the patient, Jenks (1993)
concluded that decision-making was aided when nurses better
communicated with patients and understood the idiosyncrasies
of their conditions better. Furthermore, knowing the peer
nursing staff provides an avenue for consultation and support
system when nurses needed assistance on complex decisions.
Jenks (1993) made it clear that communication plays an
important role in clinical decision-making.

To study factors contributing to clinical decision-making,
Ramezani-Badr et al. (2009) interviewed critical care nurses
from Iran. The authors reported several findings that are
prevalent in the nursing decision-making literature. Nurses
primarily used a hypothesis-driven approach and updated their
hypotheses by either collecting more information or by
explicitly testing them through interventions and patient
reactions. Additionally, nurses used familiarity approaches by
recognizing cues that matched previous patients and
situations, corroborating extant research (Cioffi, 2000, 2001).
However, Ramezani-Badr et al. (2009) reported a factor that
has been relatively under researched in applied
decision-making: consultation and communication among

All nurses reported that consulting with colleagues was an
essential criterion for making decisions that involved proper
patient care. As cases increased in complexity, greater depth
of consultation was required. This finding supports previous
research that showed nurses prefer to turn to colleagues under
complex decisions tasks (McCaughan, Thompson, Cullum,
Sheldon, & Raynor, 2005). Although this study interviewed
experienced nurses (all nurses had more than three years of
critical care experience), novices might consider new and
unfamiliar tasks as being relatively complex—a situation that
would require consultation from colleagues. While
Ramezani-Badr et al. (2009) concluded that experienced
nurses did not lack hesitation when needing assistance in
decision-making, novice nurses may not share this attribute.

Lack of communication was a key factor involved in
adverse events reported by novice nurses in Ebright et al.
(2004). Specifically, novice nurses were poor communicators
during handoffs and shift changes; they failed to report key
information on the patient. Furthermore, major issues occurred
when novices received handoffs from other novices. The
reports provided fewer cues to assist nurses in their tasks and
left the receiving novices unaware of pressing issues. This
lack of communication compromised their subsequent
decision-making and consequently led to inappropriate care to
patients. Indeed, Miller (2001) linked poor communication in
ICU to a 1.8 increase in risk-adjusted mortality.

Novice nurses did seek assistance under certain situations,
however. But Ebright et al. (2004) described this theme as
hindering decision-making because novices were assisting
novices. In fact, one nurse interviewed reported being worried
about the lack of experience when being assisted. It seems as
though this finding in Ebright et al. is a special case—it is not
often that novice nurses seek assistance from other novices.

In a related study, Manias et al. (2004) observed twelve

recently graduated Australian nurses with less than one year of
experience and commended the willingness of those nurses to
seek assistance. The study was particularly interested in the
decision-making process of novice nurses and it was
determined that novices primarily use hypothetico-deductive
reasoning. Under this framework, novices were seeking
assistance when evaluating hypotheses, and more specifically,
the novices consulted experienced nurses when contemplating
decisions on treatment options.

The result reported by Manias et al. (2004) might not be a
general finding. Although nursing students acknowledge the
importance of communication in clinical decision-making
(Garrett, 2005; Hamers et al., 1994), they lack confidence in
their ability to communicate once they begin practice (Casey
et al., 2004). In the survey conducted by Casey et al., newly
graduated nurses indicated they were not comfortable with
communication among staff, residents, and other nurses. They
had a difficult time conveying issues and problems with
physicians and peer nurses. However, there was a significant
increase in communication confidence as nurses increased
their experience from six months to one year. Furthermore,
after a year nurses were more comfortable delegating
intervention methods to ancillary personnel.

Emotions and Perceptions

Nurses’ current mental and emotional states have been

shown to influence their decision-making, both positively and
negatively (Hamers et al., 1994; Garrett, 2005; Casey et al.,
2004; Hagbaghery, Salsali, & Ahmadi, 2004; Rhodes, 1985;
Woolley, 1990). If nurses feel pressured, unconfident, or
incompetent, it can result in poor quality decisions—at least
measured by self-reports (Hagbaghery et al., 2004). Emotional
characteristics are difficult to measure and manipulate
experimentally. Therefore, studies investigating this aspect of
decision-making primarily use questionnaires or introspective

Thiele and colleagues had nursing students take the Clinical

Decision-Making in Nursing Scale (CDMNS) prior to a
completing several clinical decision-making scenarios (Thiele
et al., 1991). The CDMNS measures perceptions of
decision-making under four categories: a) searching for
alternatives or options; b) canvassing objectives and values; c)
evaluating and reevaluating consequences; and d) searching
for information and assimilating new information in an
unbiased manner. The total potential score for CDMNS is 200,
with higher scores indicating greater confidence in
decision-making. According to Thiele et al., scores of 150
indicated an average level of confidence in decision-making.

Participants scored an average of roughly 111, indicating a
lack of confidence in their decision-making abilities. The
authors interpreted these low scores as evidence that students
were hesitant about making clinical decisions. This comported
with the responses from the decision-making
scenarios—participants’ decisions were characterized by

W. J. Muntean | Clinical decision-making 14

random choice, with over selection of cues. On the surface, the
conclusions of this study seem plausible, but the authors failed
to regress CDMNS scores with decision-making scores on the
simulation. Such a test would provide better support for the
strong form of their argument.

In the survey study conducted by Casey et al. (2004), newly
graduated nurses answered a battery of questions pertaining to
their confidence in making clinical decisions. The results
revealed a U-shaped function such that nurses between zero
and three months of experience started out confident, which
then declined until roughly a year of experience, and finally
increased thereafter. This pattern is interesting because it
could be interpreted as a learning curve of applied nursing.
That is, newly entering nurses are naïve and overly confident
but once they receive some experience they understand the
complexity and dynamics of nursing—they realize the
difficulties of clinical decision-making. However, following
an acquisition period of a year, they come to understand their
roles better and are more comfortable making decisions. This
interpretation is consistent with Radwin (1998), which showed
nurses gain confidence with experience.

To investigate facilitators and inhibitors of clinical
decision-making, Hagbaghery et al. (2004) interviewed
thirty-eight participants comprising Iranian nurses, nursing
managers, and physicians. A nurse’s self-confidence was a
critical theme that emerged from the interviews. On the one
hand, nurses described that being self-confident allowed them
to take control of situations and increased the potential to
make independent decisions. On the other hand, nurses
reported that when they lacked self-confidence they felt
self-doubt, powerless, and hopeless; they even went so far as
avoiding participation in decision-making.

Self-confidence also inspired nurses to become proactive
decision-makers. Much like the nurses in Hoffman et al.
(2009), confident nurses in Hagbaghery et al. (2004) were
initiators and made preventative decisions rather than merely
responders of problems. Nurses felt more efficient and
reported that confidence accelerated their timeliness in making
and implementing decisions—which supports previous
findings (Young, 1987).

Although confidence is reported to have influential effects
on decision-making, no studies provide direct links to the
accuracy of decisions. How does confidence relate to the
efficacy of decisions? Do nurses make high-confidence errors
in their decision? If so, what are the contributing factors?
High-confidence decision errors are particularly problematic
because the nursing environment does not allow for automatic
corrective feedback, perpetuating erroneous decision-making.

Professional Orientation
Closely related to confidence is a nurse’s perception on

their value roles and occupational orientation. Rhodes (1985)
investigated the effects orientation ideology on clinical
decision-making and categorized nurses as belonging to one of
three categories. First, a paramedical occupation orientation is
a nurse who considers themselves as a subordinate to doctors
and believes their job involves carrying out medical orders.

Second, a bureaucratic occupational orientation is a nurse who
defers authority and responsibility for decision-making to
those higher in the hospital hierarchy. And third, a
professional occupational orientation is a nurse who believes
in having control over his or her own work and

Using British nurses, Rhodes concluded that a professional
occupational orientation is linked with higher levels of clinical
decision-making. Hoffman, Donoghue, and Duffield (2004)
replicated this finding with Australian nurses. In their study,
those who had a professional occupation had a greater
propensity to make clinical decisions. In addition to these
findings, Hagbaghery et al. (2004) indicated that nurses who
lacked confidence in their decision-making had poor
occupational orientations; nurses viewed themselves as agents
to complete physician’s orders.

A nurse’s perception of positive and negative consequences

has been reported to affect clinical decision-making
(Ramezani-Badr et al., 2009; Offredy, 1998; Smith et al.,
2008; Morrow, 2009). Nurses assess the risk involved with
decisions and outcomes of those decisions. When risks are
perceived to be too high, nurse can become uncomfortable
with decision-making and in turn make more errors (Smith et
al., 2008). Furthermore, the assessment of risk has been a
proxy for difficultly in decision-making, with easier decisions
representing lower risks. Hence, there are fewer errors with
low-risk decisions.

Ramezani-Badr et al. (2009) reported that nurses selected
decision options as a function of the risk-benefit tradeoff.
When the risks were increasingly high, nurses avoiding
exercising that option, regardless of whether it was the correct
option or not. Nurses in Morrow (2009) indicated that they
received pressure to go beyond the scope of their practice,
thereby potentially altering the risk-seeking threshold of
decisions. If nurses become more pressured they lean towards
making higher-risk decisions and as a result, make more

Offredy (1998) described nurses as appreciating the
inappropriate consequences of their decisions. That is to say,
nurses made deliberate efforts to avoid erroneous decisions
and the negative consequences associated with them. Nurses
proceeded to make decisions in a conservative and cautious
manner, demonstrating risk-aversion. Nursing students are
similar in this regard (Garrett, 2005). They considered the
patient outcome when making decisions and also considered
how they would feel making the decision. Students reported
feeling the anticipation of a positive reward when making a
seemingly correct decision. Conversely, they had a feeling of
internal conflict or stressor when believing they made an
incorrect decision. Students stated that these feelings
influenced their decision-making.

Personal Values
Nursing decision-making is not free of influence from

W. J. Muntean | Clinical decision-making 15

personal values and beliefs (Field, 1987; Woolley, 1990;
Mahon & Fowler, 1979; Berggren, Bégat, & Severinsson,
2002; De Casterlé, Izumi, Godfrey, & Denhaerynck, 2008;
Dreyer, Forde, & Nortvedt, 2011; Monterosso et al., 2005).
Nurses have been shown to introduce their own personal
beliefs and biases in their decision-making. Bucknall &
Thompson (1997) reported that 22% of their surveyed nurses
indicated that, at least once a week, their decision-making was
conflicted with personal values. Despite this large proportion
of responses, nurses stated that the majority of their peer
nurses held the same personal values. The confliction with
personal values arose from the separation in values and beliefs
from doctors and physicians.

Woolley (1990) wrote a report on factors that influence
clinical reasoning and termed one factor as subjective
responses. She describes several studies that have reported
biased treatment because of personal belief. Webb (1985)
surveyed thirty nurses about beliefs of early termination of
pregnancy and found that all expressed negative
attitudes—one nurse expressed that those seeking termination
should be punished for their mistake by putting them through
pain and trauma! While these views are grossly extreme, and
can be argued as less relevant today due to societal changes, it
does speak to the issue that personal values are present in
clinical decision-making (for more examples, see Stockwell,
1972; Jeffery, 1979).

Environmental Factors

In contrast to individual factors, which are a property of the

decision-maker, environmental factors are a property of the
task problem itself. These factors relate to the contextual
features that surround decisions. They interact with individual
factors and are the backdrop for every decision—whether they
facilitate, hinder, or have a neutral effect on decision-making.
For this reason, applied research on nursing clinical
decision-making can be challenging. Furthermore, the nature
of the interactions with individual factors are unknown and
under-researched. Despite the dearth of research, several
environmental variables have been well established; their
effects on clinical decision-making are largely undisputed.
These factors are discussed below.

Task Complexity

Of all the environmental factors examined in clinical

decision-making, task complexity has produced consistent
outcomes on decisions: Increasing the complexity of the
decision-task results in greater difficulty and a greater
propensity for making errors (see, e.g., Corcoran, 1986a,
1986b; Evangelisti, Whitman, & Johnston, 1986; Lewis, 1997;
Gordon, 1980; Onken, Hastie, & Revelle, 1985; Paquette &
Kida, 1988; Payne, 1976). Task-complexity can be a function
of any characteristic within the decision-making task that
increases the demands on the decision-maker’s information
processing (Lewis, 1997).

Frameworks and theories of nursing clinical
decision-making are conflicted when describing the effects of
task complexity. From the cognitive continuum perspective, as
a problem becomes less structured, more ambiguous, and
more difficult, decision-making outcomes are best when
intuitive approaches are entertained (Lamond & Thompson,
2000). Oddly, according to the skills acquisition theory (e.g.,
the humanistic-intuitive approach), if a decision-maker
encounters a task that is overwhelming difficult, they are
theorized to revert back to analytical procedural strategies
(e.g., Gobet & Chassy, 2008; O’Neill & Dluhy, 1997; Tanner,
1989, Schmidt et al., 1990; Benner et al., 1992; Cioffi, 1998).
The information-processing model makes no assumption on
the effects of task complexity—it prescribes a
hypothetico-deductive reasoning strategy regardless of
difficulty, familiarity, or other related factors (Greenwood,
2000; Radwin, 1990; Banning, 2007).

Support for each of these frameworks has been shown
empirically, and thus it is difficult to endorse one framework
over another. Although the general finding is that as decisions
become more complex, nurses use less normative thinking,
collect fewer data, and rely more on short-cut strategies
(O’Neill, 1995; O’Neill, Dluhy, Fortier, & Michel, 2004;
Cioffi & Markham, 1997; Rew, 1988). However, this is not
always observed (e.g., Hicks et al., 2003).

One study examining complexity and decision-making
strategies compared nurses’ natural decision-making strategies
with a baseline created from a decision aid (Hughes & Young,
1990). The decision aid was the Decision Analytic
Questionnaire (DAQ) and can be loosely compared to a
decision tree. It served as the “optimal” decision derived from
a systematic and analytical decision-making approach. The
authors provided scenarios of various complexities to nurses
who then generated decisions, both naturally and with the
DAQ. The authors found that nurses’ decisions were
consistent with the DAQ for low complex situations,
demonstrating a tendency to naturally use a systematic
decision strategy. However, nurses were inconsistent with the
DAQ for complex scenarios, indicating that they were relying
more on intuitive approaches to decision-making.
Collectively, these results support the assumptions made by
the cognitive continuum theory—well-structured tasks are
better suited for systematic strategies and ill-structured tasks
are better suited for intuitive strategies.

Hicks et al. (2003) replicated the findings of Hughes and
Young (1990) using similar procedures. The DAQ was
modified for the use of critical care nurses and instead of
having three levels of complexity, as was done in Hughes and
Young, only two scenarios were constructed—high and low
complexity. The nurses also took a critical disposition
inventory to measure the extent to which a person possesses
the attitudes of a critical thinker. Education and experience
levels were obtained to investigate their effects on both
decision-making strategies and critical thinking.

Neither education nor experience was correlated with
critical thinking dispositions. In addition, critical thinking
dispositions were not correlated with decision-making

W. J. Muntean | Clinical decision-making 16

consistency (e.g., the correspondence between the DAQ and
the nature strategy used by the nurse). One reason for this odd
finding is that critical thinking dispositions might not be the
most accurate measure of critical thinking abilities (Long et
al., 2007; Girot, 2004).

Perhaps the most well known study examining task
complexity in nursing clinical decision-making was conducted
by Corcoran (1986a). There were two sets of nurses used for
this study, novice nurses who had less than six months of
experience and expert nurses who had more than eighteen
months of experience. Each participant viewed three written
scenarios that varied in complexity from least to greatest.
Complexity was assessed by varying the number of
pain-related problems presented by the patient, the
interrelation of the pain-related problems, and the extent to
which protocols for pain control could be applied to the case.
Using a think aloud protocol, the subjects verbalized an action
plan to control the patient’s pain.

Novices and experts did not differ in their initial approaches
as a function of complexity. However, novice and experts did
differ from one another: Experts used a broad initial approach
and novices used a narrow approach. A broad approach is one
that gains an overview of situation and attends to several cues
before making a general hypothesis, which is the refined and
updated upon the collection of additional information. By
contrast, a narrow approach focuses immediately on one
aspect of the situation and immediately forms a hypothesis.
Although novices and experts did use different initial
approaches, complex tasks resulted in fewer hypotheses being
evaluated despite more being generated.

There was no clear pattern on whether novice nurses were
using an opportunistic approach or systematic approach to
solving problems, regardless of task complexity. However,
expert nurses used systematic approaches for the low complex
scenario and opportunistic approaches for the complex
scenarios. Corcoran (1986a) described the opportunistic
approach as being one that is multidirectional and appears to
be chaotic and disorderly because the nurse chooses to pursue
what she or he believes to be opportune at the time (e.g.,
intuitively guided decision-making). This empirical result
supports the cognitive continuum theory, which assumes
complex tasks require intuitive strategies to reach optimal

Complexity characteristics have been defined by the
literature in a variety of forms, but the majority of studies
describe complexity as the number of attributes or dimensions
of the task (Gordon, 1980; Lewis, 1997; Corcoran, 1986a,
1986b; Hicks et al., 2003). Lewis (1997) wanted to get a better
understanding of the primary features responsible for
task-complexity. She created a scenario describing a patient on
a mechanical ventilator and had nurses decide whether to
wean the client off the ventilator or not.

Complexity was assessed through four types of cues:
irrelevant cues, ambiguous cues, conflicting cues, and change
cues. Irrelevant cues were pieces of information that had no
bearing on the decision task. Ambiguous cues were pieces of
information that could affect the decision to wean but the

relationship was unclear. Conflicting cues were pieces of
information that would lead to a different decision than other
information. And change cues were pieces of information that
described a change in behavior, such as symptoms that
improved or worsened.

The presence or absence of the four cues was factorially
manipulated creating sixteen different versions of the script,
but the two “end anchors” were omitted from experimentation.
A repeated measure design was used such that each nurse saw
every version of the scenario, rating each scenario on the
complexity of decision-making task using a 7-point likert
scale. The results revealed that the presence of conflicting
cues led to greatest increase in complexity ratings, followed
by ambiguous cues and change cues, which did not differ from
one another. Irrelevant cues were shown to have the least
impact on the nurses’ ratings.

Other researchers have ascribed decision-making
complexity to additional environmental elements (Thompson,
1999a; Tanner et al., 1987). First, the number of cues a nurse
has to process is directly related with the complexity of the
decision task. Increasing the number of cues places an
increasing demand on the processing ability of the nurse.
Second, dependability of the cues is inversely related with
decision complexity. If cues are highly dependable then nurses
require fewer cues to make decisions, thereby reducing the
complexity of the task. Third, as the degree of overlap
between the cues increases—that is, more than one problem is
associated with the overall clinical challenge—then the
complexity of the task increases. And forth, if the decision
task has a limited amount of irreducible uncertainty then the
task is deemed to be challenging and complex.

As described above, a wide range of variables determine the
complexity of clinical decision-making. While the research
shows complexity increases difficulty, increases error making,
and reduces certainty—for both expert and novice
nurses—little research has been conducted on factors that
mitigate these effects. Perhaps such a task it is too great of a
challenge, seeing as the nursing environment is so dynamic
and so difficult to control for idiosyncrasies. Given that
task-complexity produces such a robust effect on clinical
decision-making, it seems that this is a viable pursuit for
future studies.

Time Pressure

In addition to the challenges brought about by

task-complexity, the nursing environment is replete with
decision-making under time constraints (Saintsing et al., 2011;
Ebright et al., 2004; Casey et al., 2004; Hickey, 2009).
Regardless of whether the decision tasks are routine or not, the
reduction of time required to make decisions introduces the
potential for erroneous decisions and increases the likelihood
of making mistakes (Ebright et al., 2004; Bucknall &
Thompson, 1997; Bourbonnais & Baumann, 1985; Thompson
et al., 2008). Bucknall & Thompson (1997) showed an
overwhelming proportion of surveyed nurses indicating that
on a weekly basis they either did not have enough time to

W. J. Muntean | Clinical decision-making 17

make decisions (40%) or enough time to implement decisions
(56%). Still, nurses are trained to perform under such
in-the-moment conditions and make immediate life-saving
decisions. However, time pressure does not always come in
the form of critical emergency, and often it is even
unbeknownst to the nurses themselves.

At first blush, the number of patients a nurse is responsible
for is a seemingly innocuous decision-making factor,
presumably because this number should never be excessive.
However, that is only under idealistic conditions, which are
not always possible. Notwithstanding the reasons for a greater
patient-to-nurse ratio, increasing the ratio is an effective way
to place a time limit on clinical decision-making. The
empirical result is simple and straightforward: In Ebright et al.
(2004), novice nurses who made decision errors were assigned
an average of 5.6 clients whereas those who did not make
errors were assigned an average of 4 clients.

Roughly 80% of novice nurses who made decision errors
indicated that time pressure played a large role (Ebright et al.,
2004). Nurses complained that they did not have time to
carefully assess the condition of their patients (Saintsing et al.,
2011; Casey et al, 2004; Hickey, 2009). Under these
conditions, nurses place a larger emphasis on familiarity
strategies, such as heuristics or intuition (Paley, Cheyne,
Dalgleish, Duncan, & Niven, 2007; Buckingham & Adams,
2000b; O’Neill, 1995). As discussed earlier, these strategies
are less effective for novices than experts, and therefore a
potential underlying cause for novice errors under these
situations (Hamm, 1988).

Novice nurses reported peer-pressure as an indirect source
of time constraint (Ebright et al., 2004; Hagbaghery et al.,
2004). Specifically, nurses are pressured to complete their
rounds and assigned tasks so that incoming nurses are
presented with a clean sheet, rather than having to complete a
previous nurse’s unfinished tasks. This self-imposed time
constraint is described as potent, especially for novice nurses
who want to avoid the reputation of being unable to complete
their work. These circumstances force nurses to forgo the
“should-do” work in order to complete the “must-do” work
(Bowers, Lauring, & Jacobson, 2001), thereby defaulting
nurses to perform retroactive decision-making rather than
using a more effective proactive decision-making procedure
(Hoffman et al., 2009).

Criticizing studies for not investigating real decisions under
real decision-making contexts, Bucknall (2003) used a
naturalistic observation paradigm to study environmental
influencers. Eighteen Australian nurses from various hospitals
were observed for two hours during routine practice and then
subsequently interviewed within the following 24-hours.
Content analyses on field notes and interview transcriptions
revealed that time constraints were carefully considered before
making decisions.

Nurses intentionally slowed their decision-making process
when there was enough time or if there was a lack of
challenge in the decision task. When this happened, they used
familiarity based decision strategies (e.g., mentally comparing
patient situations to previous encounters) and were described

as being more confident in their decisions. Conversely, nurses
under time pressure indicated rushing into decision-making,
and their decision-making was unintentionally slowed when
they lacked familiarity or when uncertainty surrounded the
decision task, though no mention was made on their
confidence in those decisions.

Although Bucknall (2003) advocated the use of real
environments when studying clinical decision-making, one
laboratory-conducted experiment shows methodological
promise for future research. Thompson et al. (2008) was
interested the efficacy of decisions under time pressures.
Registered nurses in acute care environments were sampled
from the United Kingdom (n = 95), Netherlands (n = 50),
Australia (n = 50), and Canada (n = 50) and varied in years of
experience. They were provided with 50 vignettes of patients
who had a perioperative myocardial infarction while
undergoing an elective surgical procedure. As can be seen
from Table 3, the vignettes contained simple chart information
that varied in symptoms.

According to the Modified Early Warning Score (MEWS;
Subbe, Kruger, Rutherford, & Gemmel, 2001), a standardized
assessment of the likelihood a patient is at-risk of a critical
event, eighteen of the vignettes had scores that require a nurse
to intervene by contacting a senior nurse or doctor. After
viewing each vignette, the nurse participants had to decide
whether an intervention was appropriate. Time pressure was
introduced on some trials by the presence of a clock symbol,
which informed nurses that decisions must be made within ten

This study led to several methodological benefits over
observational studies. First, the designed allowed for a large
number of observations from each nurse. Second, normative
decision outcomes were known and thus nursing scores could
be compared to an objective measure of a correct decision.
Third, because of the previously mentioned points,
signal-detection analyses could be carried out to establish the
sensitivity (accuracy) and response bias of the nurses.

In the context of the experiment, signal-detection theory
assumes that the vignettes form two normal distributions
underlying the strength of evidence in favor of an intervention
(see Figure 2). The two distributions represent vignettes that
require and do not require an intervention (i.e., signal
distribution and noise distribution, respectively). Sensitivity
(i.e., accuracy) is a measure of overlap between the




Heart rate 50 – 155 bpm
Systolic blood pressure 72 – 221 mm Hg
Respiratory rate 9 – 48 bpm
Urine output (last 4 hours) 0 – 960 ml
Oxygen saturation 67% – 100%
Conscious level Fully conscious to unconsious
Oxygenation support Breathing room air to CPAP
Time pressure Present or absent

Note—CPAP = continuous positive airway pressure. Reproduced from
Thompson et al. (2008).

W. J. Muntean | Clinical decision-making 18

distributions; accuracy increases as the distance between the
distribution means increase. Signal-detection has the added
benefit of measuring the criterion for endorsement. That is,
any vignette with a strength-of-evidence greater than the
criterion will result in an intervention—regardless of which
distribution the vignette belongs to. A liberal criterion will
result in a greater proportion of vignettes classified as needing
an intervention, whereas a conservative criterion has the
opposite effects—a lower proportion of cases are identified as
needing interventions.

Thompson et al. reported that the countries differed on bias,
such that Australian nurses had less of a tendency to take
action than did the other countries. Apart from that effect,
country did not interact with any other variable and was
omitted from further analyses. Time pressure resulted in both
less accurate decisions and a lower tendency to intervene.
Perhaps the accuracy data can be seen as unsurprising, but this
study is the first of its kind to demonstrate such effects
empirically, rather than through interviews or the like.
Furthermore, this study revealed that time pressure biases
nurses away from making interventions, which explains a
source of errant decision-making.

For accuracy, years-of-experience interacted with time
pressure. When there was no time restriction, greater
experience led to more accurate decisions. However, novice
nurses were just as accurate as expert nurses when time was
limited. This pattern is somewhat surprising because intuitive
reasoning, which is purportedly more predominant in
experienced nurses, is a quick process that can immunize
decision-making from the negative effects of time pressure. If
experienced nurses relied more on intuitive reasoning then that
would suggest no interaction. However, an interaction was
obtained, which is theoretically puzzling.

There was no interaction of experience and time pressure

for threshold placement, but there was a main effect of
experience. Novice nurses were less cautious under time
pressure than more experienced nurses, thereby failing to take
action on cases that required interventions. This finding is
somewhat counterintuitive. One would predict the opposite
pattern; nurses with less experience would lean on the side of
intervening because of the lower cost of error, whereas failing
to intervene might lead to severe consequences. However,
according to Thompson et al., novice nurses have yet to learn
these associations and instead are too focused on irritating
doctors and critical care outreach nurses by contacting them
with false alarms.

Collectively, these studies show the dramatic effects of time
pressure on decision-making. Nurses are more prone to errors
when rushing their orders and decision-making process. The
source of time constraints can come in many environmental
forms and may even be self-imposed. Regardless of the
source, limiting the time needed to make decisions will result
in less efficient decision-making for novice as well as expert


Increasing the workload of nurses not only places time

constraints on nurses, but also increases the propensity to
become interrupted while performing their duties and tasks
(Hedberg & Larsson, 2004). For general decision-making,
disruptions have been shown the produce both positive and
negative consequences, depending on the complexity of the
task (Speier, Valacich, & Vessey, 1999). Simple
decision-making tasks require relatively fewer cues to be
processed than complex tasks and therefore place a lower
cognitive demand on the decision-maker. Under simple
conditions, disruptions have been shown to narrow attention,

Fig. 2. An illustration of the signal and noise distributions of the vignettes.

W. J. Muntean | Clinical decision-making 19

increase arousal, and reduce the number irrelevant cues
processed by the decision-maker. As a result, decisions are
made quicker and with little or no loss of task-relevant cues;
accuracy is not sacrificed.

By contrast, complex decision tasks place a much higher
cognitive load on the decision-maker. They must attend to
more cues and process them relationally to reach an
appropriate decision. Narrowing attention—as a byproduct of
disruption—will result in the loss of information processing,
some of which will be relevant cues. There will be a greater
deterioration in performance as the number of disruptions
increase. Furthermore, to save cognitive resources a
decision-maker will rely more on heuristic approaches, which
have systematic shortcomings and produce less accurate
decisions (Baron, 1986; Kahneman & Tversky, 1996).

Disruptions happen quite often in nursing environment.
Hedberg & Larsson (2004) observed six Swedish nurses for
thirty hours to discover environmental factors that affect
decision-making. Two general themes emerged from their
field notes, interruptions and work procedures. Because the
researchers used observational methods they were not able to
verify the efficacy of nursing decisions, but nurses were
reported to be frustrated at times when disrupted or
interrupted. Hedberg and Larrson took this as evidence that
interruptions negatively impacted clinical decision-making.
While this implicit argument might be weak, it does lay the
groundwork for future experimentation and corroborates other
decision-making findings (see, Speier et al., 1999).

Interruptions occurred through various forms, but the most
predominant type was interruptions through assistant nurses
and patients, which accounted for over half of the observed
distractions. These interruptions happened regardless of
location and task; they occurred in all daily routine tasks.
While attending to patients, nurses were most often interrupted
by client questions, although staff members (especially
assistant nurses) also contributed to the distractions—either by
asking procedural questions or requesting assistance with
other patients.

Hedberg and Larrson (2004) characterized these
interruptions as impeding decision-making. This might be an
overgeneralization, though, at least in the case of patient
questions. On the one hand, increasing nurse-patient
interactions could lead to the discovery of more information
cues, facilitating the decision-making process. On the other
hand, patients might ask questions that lead to processing of
unrelated information, increasing the cognitive load of the
nurse and displacing relevant cues. The authors were not able
to make a clear connection one way or the other. They
defaulted to the assumption that interrupting nurses by asking
questions hinders decision-making, but more research is
needed to establish a direct causal link.

Technical interruptions accounted for a much lower
proportion of the distractions that nurses faced in Hedberg and
Larrson (2004), roughly 13%, with the main source coming
from phone calls or emergency alarms of various sorts. Nurses
in this study needed to be on hand at all times and when they
heard a phone ring they interrupted their work to answer.

Patient rooms also had phones, which rang on a regular basis
causing further disruptions when nurses were giving care.
During interruptions, two thirds of the nurses were performing
direct patient care (e.g., personal care, administering
medication, preparing patients for activities such as meals or
resting), while the rest were performing indirect patient care
(e.g., sorting and documenting laboratory tests or preparing
patients records). Regardless of what type of the task nurses
were performing, interruptions were prevalent and disrupted
nursing duties and decision-making.

Area of Specialty and Professional Autonomy

A nurse’s area of specialty and the department that he or she

works for has some influence on clinical decision-making. In
particular, departments differ in the average risks associated
with decisions. For example, a poor decision made by a
surgical nurse might lead to greater consequences than a poor
decision made by a nurse who is prepping a patient’s dinner.
Decision-making under high risks is associated with more
complex tasks and has been linked to more erroneous
decisions (Smith et al., 2008).

Setting aside the associated risk, the average complexity of
a decision task also differs as a function of a nurse’s
department, and can result in dissimilar quality of
decision-making (Thompson, 1999a). This adds to the
difficulty in assessing decision-making in applied settings,
especially because nurses from different areas of specialties
will inherently have unequal base rates for decision errors.
Furthermore, area of specialty can affect the propensity to
make decisions, allowing more opportunity to make errors
(Hoffman, Donoghue, & Duffield, 2004; but see, Rhodes,

Professional autonomy, the freedom to make unsupervised
decisions, also varies as a function of environment.
Empowering nurses to make independent decisions, or at the
very least increasing their independence, has been shown to
have positive effects on clinical decision-making (Bakalis et
al., 2003; Schutzenhofer & Musser, 1994; Hooft, 1990;
Hagbaghery et al., 2004; Ramezani-Badr et al., 2009). Bakalis
et al. (2003) compared decision-making of Greek and English
nurses using clinical decision-making cards. As described
earlier, nurses in this study saw eight scenarios that contained
a set of sequential decisions—each made by selecting the
appropriate option among several alternatives. In addition to
the presented alternatives, nurses were allowed to select a
“call the doctor” option to indicate when they would hand off
decision responsibilities over to other medical staff. The
researchers used this as a proxy to gauge professional

The scenarios covered acute and recovery phases of
post-myocardial infraction and English nurses were
discovered to make better quality decisions during recovery
phases. This was attributed to greater professional autonomy
that English nurses held; they chose to hand off responsibility
later in the decision set. Borrowing from Hooft (1990),
Bakalis et al. (2003) theorized that professional autonomy

W. J. Muntean | Clinical decision-making 20

involves the nurse’s freedom to act in the best interest of the
patient, and therefore more emphasis is placed on the patient
care. This assumption may be premature because autonomy
could be viewed as a social phenomenon, which is influenced
by different perceptions of nursing held by Greek and English

Several factors have been correlated with autonomous
practitioners. Schutzenhofer & Musser (1994) surveyed over
500 registered nurses using a Nursing Activity Scale (NAS),
which requires nurses to answer questions where they must
exercise some degree of professional autonomy. For
autonomous practice, there was no main effect of education,
but simple pairwise comparisons showed that those holding a
MSN were more autonomous than either nurses with a
diploma, ADN, or BSN. There was a difference in NAS scores
for different areas of specialty: psychiatric/mental health
nurses were more autonomous than medical-surgical,
maternal-newborn, and critical care nurses. These data lend
further support to the assumption that decision-making is
dependent on environmental context, although to be fair,
novice nurses are not likely to be placed in advance areas.

On a similar note, novice nurses are not autonomous to
begin with. They are required to report to senior nurses when
encountering any issues of concern. According to Hagbaghery
et al. (2004), this could reduce a novice’s confidence in their
ability to make effective decisions. Increasing a novice nurse’s
autonomy might not be the solution, however. Nurses with
more professional autonomy place a greater reliance on
risk-benefit criteria rather than organizational necessities when
making decisions (Ramezani-Badr et al., 2009). While this is
not a concern for nurses who understand risk-benefit tradeoffs
well, novices have yet to learn these associations—at least in
real world situations. Despite the handful of studies
researching professional autonomy on clinical
decision-making, more research is needed to clearly
understand the effects on novice nurses.


When entering professional nursing, novices are

accompanied with a large set of responsibilities involving their
decision-making. The literature reviewed made it clear that
nursing students are inadequately trained in critical thinking
and decision-making—at least decision-making found in real
life settings (see, e.g., Saintsting et al., 2011; Smith &
Crawford, 2002). Submerging novices in clinical
decision-making seems to provide a solution but only for those
who can handle it, as indicated by confidence in
decision-making of nurses who pass the one-year mark of
practice (Casey et al., 2004). This is a potential costly solution
and puts both patients and nurses at risk, especially for novices
who are dramatically inclined at decision-making.
Furthermore, many variables have been identified to impede
effective decision-making, slowing down the process of
gaining competency.

Factors identified in this literature review either affect the
decision-maker or the decision-task—perhaps even an
interaction of both. A mix of the individual factors can be
taught and tested (e.g., cue recognition and hypothesis
updating). Cue recognition is the foundation of all
decision-making and is built through knowledge that is gained
in nursing school. While this can be supplemented with
clinical experience, novice nurses must enter the profession
with an acceptable level knowledge. This can be easily tested
to ensure that novices do not lack the fundamentals. However,
Lewis (1997) showed that cue recognition is multidimensional
and not all types of cue recognition are equal. Will novice
nurses be able to make effective decisions when facing
decision-tasks that contain conflicting cues?

But not all the individual factors discovered through the
review can be tested (e.g., education, clinical experience, or
propensity to communicate). These factors are a byproduct of
exploratory studies—ones that rely on observational and
survey studies. These studies are insightful and provide the
motive for future confirmatory studies, but the method of data
collection places a limit on the type of factors that can be
researched (Aitken et al., 2011). Methodological innovations
are underway and recent studies show promise that more
testable factors will be discovered (Thompson et al., 2008).

Much the same can be said about environmental
factors—some can be explicitly test (e.g., time pressure and
task complexity), but others cannot (e.g., professional
autonomy). Increasing task complexity is a reliable way to
introduce decision-making errors (Corcoran, 1986a), and
luckily, it differs individually. Essentially, task complexity is
relative. For instance, novice nurses who have stronger mental
representations and nursing schemas can chunk greater
amount of information compared to those with poorer
representations. Nurses who process more information
simultaneously will have less cognitive load when filtering
through cues and will perceive decision-tasks as less complex.

Overall, nursing research on clinical decision-making is
very challenging because of the dynamic environment in the
applied setting. The research reviewed in this paper clearly
demonstrates this. While no single experiment or study can
account for all the variables affecting clinical
decision-making, researchers have made good attempts to
isolate individual factors and explore them to the extent


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